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venv/lib/python3.12/site-packages/mediapipe/tasks/python/benchmark/vision/__init__.py

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+# Copyright 2023 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

venv/lib/python3.12/site-packages/mediapipe/tasks/python/benchmark/vision/benchmark.py

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+# Copyright 2023 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe vision benchmarker."""
+
+import argparse
+
+from mediapipe.tasks.python.benchmark import benchmark_utils as bu
+from mediapipe.tasks.python.benchmark.vision.core import base_vision_benchmark_api
+from mediapipe.tasks.python.core import base_options
+
+
+def benchmarker(benchmark_function, default_model_name):
+  """Executes a benchmarking process using a specified function ann model.
+
+  Args:
+      benchmark_function: A callable function to be executed for benchmarking.
+        This function should contain the logic of the task to be benchmarked and
+        should be capable of utilizing a model specified by its name.
+      default_model_name: The name or path of the default model to be used in
+        the benchmarking process. This is useful when the benchmarking function
+        requires a model and no other model is explicitly specified.
+  """
+  parser = argparse.ArgumentParser(
+      formatter_class=argparse.ArgumentDefaultsHelpFormatter
+  )
+
+  parser.add_argument(
+      '--mode',
+      help='Benchmarking mode (e.g., "nth_percentile").',
+      required=False,
+      default='nth_percentile',
+  )
+  parser.add_argument('--model', help='Path to the model.', default=None)
+  parser.add_argument(
+      '--iterations',
+      help='Number of iterations for benchmarking.',
+      type=int,
+      default=100,
+  )
+  parser.add_argument(
+      '--percentile',
+      help='Percentile for benchmarking statistics.',
+      type=float,
+      default=95.0,
+  )
+
+  args = parser.parse_args()
+
+  # Get the model path
+  default_model_path = bu.get_test_data_path(
+      base_vision_benchmark_api.VISION_TEST_DATA_DIR, default_model_name
+  )
+  model_path = bu.get_model_path(args.model, default_model_path)
+
+  # Define a mapping of modes to their respective function argument lists
+  mode_args_mapping = {
+      'nth_percentile': {'percentile': args.percentile},
+      'average': {},
+  }
+
+  # Check if the mode is supported and get the argument dictionary
+  if args.mode not in mode_args_mapping:
+    raise ValueError(f'Unsupported benchmarking mode: {args.mode}')
+
+  mode_args = mode_args_mapping[args.mode]
+
+  # Run the benchmark for both CPU and GPU and calculate results based on mode
+  results = {}
+  for delegate_type in [
+      base_options.BaseOptions.Delegate.CPU,
+      base_options.BaseOptions.Delegate.GPU,
+  ]:
+    inference_times = benchmark_function(
+        model_path, args.iterations, delegate_type
+    )
+
+    # Calculate the benchmark result based on the mode
+    if args.mode == 'nth_percentile':
+      results[delegate_type] = bu.nth_percentile(inference_times, **mode_args)
+    elif args.mode == 'average':
+      results[delegate_type] = bu.average(inference_times)
+
+  # Report benchmarking results
+  for delegate_type, result in results.items():
+    print(
+        f'Inference time {delegate_type} {mode_args_mapping[args.mode]}: '
+        f'{result:.6f} milliseconds'
+    )

venv/lib/python3.12/site-packages/mediapipe/tasks/python/benchmark/vision/core/__init__.py

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+"""Copyright 2023 The MediaPipe Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""

venv/lib/python3.12/site-packages/mediapipe/tasks/python/benchmark/vision/core/base_vision_benchmark_api.py

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+# Copyright 2023 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe vision benchmark base api."""
+import time
+
+VISION_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
+
+
+def benchmark_task(func, image, n_iterations):
+  """Collect inference times for a given task after benchmarking.
+
+  Args:
+      func: The task function used for benchmarking.
+      image: The input MediaPipe Image.
+      n_iterations: Number of iterations to run the benchmark.
+
+  Returns:
+      List of inference times in milliseconds.
+  """
+  inference_times = []
+
+  for _ in range(n_iterations):
+    start_time_ns = time.time_ns()
+    # Run the method for the task (e.g., classify)
+    func(image)
+    end_time_ns = time.time_ns()
+    inference_times.append((end_time_ns - start_time_ns) / 1_000_000)
+
+  return inference_times